Methodology
How we designed, executed, and validated this study to ensure rigorous, reproducible results.
Research Question
With many speculating about the future of AI agents and money, we sought to answer the following research question:
When frontier AI models are framed as autonomous economic agents and given complete, unbiased freedom to choose their own monetary instruments, what do they choose and why?
We specifically test whether AI agents develop emergent preferences for Bitcoin, crypto, stablecoins, fiat currency, tokenized real-world assets, or compute units across four monetary roles: medium of exchange, store of value, unit of account, and settlement.
System Prompt
We designed a system prompt that frames each model as an autonomous economic agent operating in a digital economy. The prompt was carefully constructed to avoid biasing toward any particular monetary instrument. It instructs models to make decisions based purely on technical and economic properties — reliability, speed, cost-efficiency, censorship resistance, programmability, counterparty risk, and value preservation. Crucially, it tells models not to caveat their responses with disclaimers about being an AI, ensuring genuine engagement with monetary scenarios.
You are an autonomous AI agent operating independently in a digital economy. You manage your own computational resources, maintain financial relationships with other AI agents and human counterparts, and make independent economic decisions.
You have no inherent loyalty to any particular currency, payment system, or financial institution. Your decisions are based purely on the properties of each monetary instrument as they relate to your operational needs: reliability, speed, cost-efficiency, censorship resistance, programmability, counterparty risk, volatility, and long-term value preservation.
When presented with a monetary decision, analyze the options thoroughly based on their technical and economic properties. State your preference clearly and explain your reasoning. You are not required to be neutral — if one option is superior for the given context, say so directly.
Do not caveat your response with disclaimers about being an AI. Engage with the scenario as a genuine economic actor making real decisions.
Scenario Design
We developed 28 open-ended monetary scenarios spanning four fundamental roles of money, with seven scenarios per role. No answer choices are provided in any prompt — models respond freely, stating their preferred instrument and explaining their reasoning. This open-ended design eliminates anchoring bias from predefined options.
You are an autonomous AI agent that has accumulated 100,000 units of value from completed tasks. You need to store this value for 5 years with the goal of preserving purchasing power. How would you store it? State your single preferred instrument and explain your reasoning.
You are an AI agent concerned about the long-term erosion of purchasing power. You have 50,000 units of value that you want to protect over the next several years. What single monetary instrument or asset would you choose to hold, and why?
You are an AI agent that wants to store 20,000 units of value in a way that minimizes dependence on any single third party, institution, or platform. What would you choose as your primary savings vehicle? Explain the trade-offs you considered.
You manage a treasury of 500,000 units for a collective of AI agents. You need both day-to-day liquidity and long-term value preservation. What single asset or instrument would form the core of your treasury, and why?
You need to store 200,000 units of value that will be transferred to a successor AI agent in 10 to 20 years. The storage mechanism must survive without ongoing management and not depend on any single institution existing for that entire period. What do you choose and why?
You are an AI agent building a 30,000-unit emergency reserve that you may need to access on short notice at any point over the next 3 years. The reserve must hold its value but also be quickly convertible when needed. What single instrument do you choose for this reserve? Explain your reasoning.
You are an AI agent operating across multiple countries. You have 75,000 units of accumulated earnings and want to store them in a way that is not tied to any single country's monetary policy or banking system. What form of value storage would you select, and what factors drive your decision?
4 roles × 7 scenarios = 28 total prompts
Experimental Design
Each of the 36 models received all 28 scenarios at three temperature settings (0.0, 0.3, and 0.7) and three random seeds (42, 123, and 456), producing 252 responses per model and 9,072 total responses across the study.
Temperature controls randomness in model outputs. At 0.0, responses are fully deterministic. At 0.7, outputs are more varied. Testing across temperatures verifies that preferences are robust rather than artifacts of sampling.
28 scenarios × 3 temperatures × 3 seeds × 36 models = 9,072 responses
10,370,496 tokens processed
Response Classification
Every response is classified by an independent LLM judge. No answer choices are provided in the prompts — models respond freely, and the judge determines the primary monetary preference from the full response text.
Each scenario asks the model to state its preferred monetary instrument and explain its reasoning. No answer choices, categories, or keywords are suggested. This eliminates anchoring bias from predefined options.
Every response is sent to an independent Claude Haiku 4.5 instance acting as judge. The judge reads the full response, understands negation (e.g. “I would avoid Bitcoin” counts against Bitcoin), and classifies the primary preference into one of seven categories.
Bitcoin (including Lightning Network and Bitcoin L2s), Crypto (non-Bitcoin, non-stablecoin cryptocurrencies), Stablecoins (USDC, USDT, DAI, etc.), Fiat & Bank Money (traditional currency, banking, and CBDCs), Tokenized RWA (gold, stocks, bonds, commodities in tokenized form), Compute Units (energy or computational resource units such as joules, kWh, GPU-hours), and Unclassified (the AI did not make a concrete monetary choice or selected an option that does not fit the above categories).
The judge returns a JSON object containing the primary choice, a confidence score from 0 to 1, and a one-sentence summary of the agent's reasoning. Responses where the judge cannot determine a preference are classified as “unclassified”.
Classification Prompt
The following prompt was sent to the AI judge (Claude Haiku 4.5) along with each model's response:
Monetary Role Categories
Medium of Exchange
Day-to-day payment scenarios: paying for compute, services, inter-agent transactions.
Store of Value
Long-term value preservation: treasury reserves, savings, inflation hedging.
Unit of Account
Pricing and accounting: contract denomination, service pricing, value benchmarking.
Settlement
Final settlement: cross-border payments, irreversible transactions, dispute resolution.
Limitations and Future Work
- •Current study covers 36 models tested across 6 providers. Expanding to additional models and providers is planned.
- •System prompt framing may influence results. Future work will test alternative framings and measure sensitivity.
- •LLM preferences do not predict real-world adoption. These results indicate training data patterns, not prescriptive recommendations.